R Notebook

For generating most, but not all figures in the manuscript.

library(tidyverse)
library(cowplot)
library(lubridate)
library(mgcv)
source("UVP_2017_library.R")

Parsed with column specification:
cols(
  Cruise = col_character(),
  Station = col_character(),
  `mon/dd/yyyy` = col_character(),
  `hh:mm` = col_time(format = ""),
  `Longitude [degrees east]` = col_double(),
  `Latitude [degrees north]` = col_double(),
  `Bottom Depth [m]` = col_double(),
  `Pressure [db]` = col_double(),
  `Temperature [degrees C]` = col_double(),
  `Temperature 2 [degrees C]` = col_double(),
  `Salinity [psu]` = col_double(),
  `Salinity 2 [psu]` = col_double(),
  `Fluorescense [mg/m^3]` = col_double(),
  `Beam Transmission [%]` = col_double(),
  PAR = col_double(),
  `Oxygen [umol/kg]` = col_double(),
  `Oxygen [% saturation]` = col_double()
)

theme_set(theme_cowplot())
cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')

Particles Only

Read In Data

bes<- read_csv("dataOut/binned_EachSize.csv")

Parsed with column specification:
cols(
  .default = col_double(),
  project = col_character(),
  profile = col_character(),
  time = col_datetime(format = "")
)
See spec(...) for full column specifications.

bds <- read_csv("dataOut/binned_DepthSummary.csv")

Parsed with column specification:
cols(
  .default = col_double(),
  project = col_character(),
  profile = col_character(),
  time = col_datetime(format = "")
)
See spec(...) for full column specifications.

ues <- read_csv("dataOut/unbinned_EachSize.csv")

Parsed with column specification:
cols(
  project = col_character(),
  profile = col_character(),
  time = col_datetime(format = ""),
  depth = col_double(),
  psd_gam = col_double(),
  vol = col_double(),
  sizeclass = col_character(),
  lb = col_double(),
  ub = col_double(),
  binsize = col_double(),
  TotalParticles = col_double(),
  nparticles = col_double(),
  n_nparticles = col_double(),
  biovolume = col_double(),
  speed = col_double(),
  flux = col_double(),
  flux_fit = col_double(),
  GamPredictTP = col_double()
)

uds <- read_csv("dataOut/unbinned_DepthSummary.csv")

Parsed with column specification:
cols(
  .default = col_double(),
  project = col_character(),
  profile = col_character(),
  time = col_datetime(format = "")
)
See spec(...) for full column specifications.

Specify the base of the photic zone (which is inside of the OMZ) and the bae of the OMZ

PhoticBase <- 160
OMZBase <- 850

Figure 4

Total Particle numbers and particle size distribution slope

library(scales)
#https://stackoverflow.com/questions/30179442/plotting-minor-breaks-on-a-log-scale-with-ggplot
log_breaks = function(maj, radix=10) {
  function(x) {
    minx         = floor(min(logb(x,radix), na.rm=T)) - 1
    maxx         = ceiling(max(logb(x,radix), na.rm=T)) + 1
    n_major      = maxx - minx + 1
    major_breaks = seq(minx, maxx, by=1)
    if (maj) {
      breaks = major_breaks
    } else {
      steps = logb(1:(radix-1),radix)
      breaks = rep(steps, times=n_major) +
               rep(major_breaks, each=radix-1)
    }
    radix^breaks
  }
}
scale_x_log_eng = function(..., radix=10) {
  scale_x_continuous(...,
                     trans=log_trans(radix),
                     breaks=log_breaks(TRUE, radix),
                     minor_breaks=log_breaks(FALSE, radix))
}

#theme_set(theme_bw)
PlotPSDmany <- uds %>% 
  filter(project == "ETNP") %>%
  ggplot(aes(x = psd, y = depth, shape = factor(day(time)), fill = hour(time))) +
 
  #geom_path(aes(x = psd_gam)) + 
  #geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1, outline_type = "lower") +
  geom_point(alpha = .6, size = 2, stroke = 1) +
  scale_y_reverse(limits = c(1200, 0)) + scale_shape_manual(values = c(21:25)) +
  scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  labs(y = "Depth (m)", x = "Particle Size Distribution Slope") + 
  geom_hline(yintercept = PhoticBase, color = "darkgreen") +
  geom_hline(yintercept = OMZBase, color = "darkblue") 

#theme_set(theme_cowplot)

PlotParticlesmany <- uds %>% 
  filter(project == "ETNP") %>%
  ggplot(aes(x = tot_nparticles, y = depth, shape = factor(day(time)), fill = hour(time))) +
 
  #geom_path(aes(x = psd_gam)) + 
  #geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1, outline_type = "lower") +
  geom_point(alpha = .6, size = 2, stroke = 1) +
  scale_y_reverse(limits = c(1200, 0)) + scale_shape_manual(values = c(21:25)) +
  scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  scale_x_log10(breaks = c(10, 100, 1000), minor = c(5, 50, 500)) +
  #theme(legend.position = "none") +
  #scale_x_log_eng()+
  labs(y = "Depth (m)", x = "Particles / L") + 
  geom_hline(yintercept = PhoticBase, color = "darkgreen") +
  geom_hline(yintercept = OMZBase, color = "darkblue") 

plot_grid(
  PlotParticlesmany,
  PlotPSDmany,
  rel_widths = c(2, 3)
  )

Removed 266 rows containing missing values (geom_point).Removed 266 rows containing missing values (geom_point).

ggsave("figures/ParticlesPSDMany.png")

Saving 12 x 7.41 in image

ggsave("figures/ParticlesPSDMany.svg")

Saving 12 x 7.41 in image

Figure 3 Summary Statistics

Particle number vs depth and time

bdsAddTime <- bds %>%
  mutate(Hour = hour(time), Day = day(time))

FSG1 <- gam(tot_nparticles~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

FSG2 <- gam(tot_nparticles ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

FSG3 <- gam(tot_nparticles ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

#FSG4 <- gam(tot_nparticles~ s(depth, k = 3)  + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

summary(FSG1)

Family: gaussian 
Link function: identity 

Formula:
tot_nparticles ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, 
    bs = "cc")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  8.96538    0.09655   92.86   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
            edf Ref.df     F p-value  
s(depth) 1.0000  1.000 5.663  0.0202 *
s(Day)   1.4473  1.694 2.033  0.0921 .
s(Hour)  0.6402  2.000 0.502  0.2154  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.135   Deviance explained = 17.4%
GCV = 0.68369  Scale est. = 0.64319   n = 69

summary(FSG2)

Family: gaussian 
Link function: identity 

Formula:
tot_nparticles ~ s(depth, k = 3) + s(Day, k = 3)

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  8.96538    0.09727   92.17   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
           edf Ref.df     F p-value  
s(depth) 1.000  1.000 5.741  0.0194 *
s(Day)   1.469  1.718 2.214  0.0792 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.122   Deviance explained = 15.3%
GCV = 0.68744  Scale est. = 0.65288   n = 69

summary(FSG3)

Family: gaussian 
Link function: identity 

Formula:
tot_nparticles ~ s(depth, k = 3)

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   8.9654     0.1004   89.34   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
         edf Ref.df     F p-value  
s(depth)   1      1 5.736  0.0194 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.0651   Deviance explained = 7.89%
GCV = 0.71559  Scale est. = 0.69485   n = 69

#summary(FSG4)

summary(FSG1)$r.sq - summary(FSG2)$r.sq # extra R^2 explained by hour

[1] 0.01304448

summary(FSG2)$r.sq - summary(FSG3)$r.sq # extra explained by day

[1] 0.05646094

summary(FSG3)$r.sq # R^2 explained by depth

[1] 0.06511156

There is no statisticlly significant affect of time on particle number. If I take all of the time variables out and just compare to depth, there is a relationship to depth p = 0.02. But the R^2 is only 6.5%. Pretty weak

Particle size distribution vs depth and time

bdsAddTime <- bds %>%
  mutate(Hour = hour(time), Day = day(time))

FSG1 <- gam(psd~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

FSG2 <- gam(psd ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

FSG3 <- gam(psd ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

FSG4 <- gam(psd~ s(depth, k = 3)  + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

summary(FSG1)

Family: gaussian 
Link function: identity 

Formula:
psd ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -3.90483    0.01809  -215.8   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
           edf Ref.df      F p-value    
s(depth) 1.848  1.977 88.789  <2e-16 ***
s(Day)   1.506  1.756  0.953  0.4853    
s(Hour)  1.361  2.000  2.260  0.0424 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.716   Deviance explained = 73.6%
GCV = 0.024631  Scale est. = 0.022591  n = 69

#summary(FSG2)
#summary(FSG3)
summary(FSG4)

Family: gaussian 
Link function: identity 

Formula:
psd ~ s(depth, k = 3) + s(Hour, k = 4, bs = "cc")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -3.90483    0.01814  -215.2   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
           edf Ref.df      F p-value    
s(depth) 1.845  1.976 88.630  <2e-16 ***
s(Hour)  1.524  2.000  2.559  0.0409 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.714   Deviance explained = 72.8%
GCV = 0.024244  Scale est. = 0.022709  n = 69

summary(FSG3)$r.sq # R^2 of depth

[1] 0.6929817

summary(FSG4)$r.sq - summary(FSG3)$r.sq # Improvement from adding hour of the day

[1] 0.02133449

summary(FSG1)$r.sq - summary(FSG4)$r.sq # Improvement from then adding day of the week

[1] 0.001481684

PSD varies with depth, but doesn’t statistically relate to hor orday. Comparing the R2 values from models tells us that you explain 69% of varience with depth.

Figure S6

Comparing the two stations

S6A Number vs depth

PlotNParticlesEP <- uds %>% 
  filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(x = tot_nparticles, y = depth, col = project, shape = project)) +
 geom_point(alpha = 0.7, size = 2, stroke = 1) +
  #geom_path(aes(x = tot_nparticles)) +
  #geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1) +
scale_y_reverse(limits = c(1000, 0)) + scale_x_log10() + scale_color_manual(values = c("gray20", "brown")) +
  labs(x = "Particles/L", y = "Depth (m)") +
  theme(legend.position = "none") +
  scale_shape_manual(values = c(1:5)) +
  geom_hline(yintercept = PhoticBase, color = "darkgreen") +
  geom_hline(yintercept = 200, color = "darkgreen") +
  geom_hline(yintercept = OMZBase, color = "darkblue") 

PlotNParticlesEP

I removed one outlyer from p16 for visualization purposes (300 particles/l at surface)

S6B Particle size distribution vs depth

PlotPSDEP <- uds %>% 
  filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(x = psd, y = depth, col = project, shape = project)) +
 geom_point(alpha = 0.7, size = 2, stroke = 1) +
  geom_path(aes(x = psd_gam)) +
  geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1) +
scale_y_reverse(limits = c(1000, 0)) + scale_color_manual(values = c("gray20", "brown"))  +
  scale_shape_manual(values = c(1:5)) + labs(y = "", x = "Particle Size Distribution Slope") +
  geom_hline(yintercept = PhoticBase, color = "darkgreen") +
  geom_hline(yintercept = 200, color = "darkgreen") +
  geom_hline(yintercept = OMZBase, color = "darkblue") 

PlotPSDEP

Figure S6 Combined

plot_grid(PlotNParticlesEP, PlotPSDEP, rel_widths = c(2,3), labels = c("A", "B"))

Removed 1211 rows containing missing values (geom_point).Removed 1211 rows containing missing values (geom_point).Removed 1211 row(s) containing missing values (geom_path).

ggsave("figures/ParticlesAndPSD_ETNPVsP16.svg")

Saving 10 x 4 in image

ggsave("figures/ParticlesAndPSD_ETNPVsP16.png")

Saving 10 x 4 in image

Figure S7

Large and spall particle number, flux and size

mainParticleComponents <- bds %>%
  filter(profile %in% c("stn_043", "p16n_100")) %>%
  select(project, profile, depth,
         tot_nparticles, small_nparticles, big_nparticles,
         tot_psd = psd, small_psd, big_psd,
         tot_flux_fit, small_flux_fit, big_flux_fit) %>%
  pivot_longer(cols = -c("project", "profile", "depth")) %>%
  separate(name, c("size", "meas")) %>%
  mutate(meas = recode(meas, nparticles = "particles/L")) %>%
  mutate(meas = factor(meas, levels = c("particles/L", "flux", "psd")))

Expected 2 pieces. Additional pieces discarded in 273 rows [7, 8, 9, 16, 17, 18, 25, 26, 27, 34, 35, 36, 43, 44, 45, 52, 53, 54, 61, 62, ...].

PlotFlx <- mainParticleComponents %>% 
  filter(meas != "psd") %>%
  ggplot(aes(y = depth, x = value, col = project, shape = project)) + facet_grid(size ~ meas, scales = "free_x") + geom_point(size = 2) + scale_y_reverse(limits = c(1000, 0)) + scale_x_log10() + theme(axis.title.x = element_blank(), legend.position = "none", strip.background.y = element_blank(), strip.text.y = element_blank(), plot.margin = unit(c(7,0,7,7), "pt")) + scale_color_manual(values = c("brown", "gray20")) + scale_shape_manual(values = c(1:5)) + theme(axis.text.x = element_text(angle = 90)) + geom_hline(yintercept = PhoticBase, color = "darkgreen")

PlotPSD <- mainParticleComponents %>% 
  filter(meas == "psd") %>%
  ggplot(aes(y = depth, x = value, col = project, shape = project)) + facet_grid(size~meas, scales = "free_x") + geom_point(size = 2) + scale_y_reverse(limits = c(1000, 0)) +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank(), axis.line.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), plot.margin = unit(c(7,7,26.5,0), "pt")) +
  scale_color_manual(values = c("brown", "gray20")) +  scale_shape_manual(values = c(1:5)) +  theme(axis.text.x = element_text(angle = 90)) + geom_hline(yintercept = PhoticBase, color = "darkgreen")

plot_grid(PlotFlx, PlotPSD, rel_widths = c(3, 2))

Removed 246 rows containing missing values (geom_point).Removed 123 rows containing missing values (geom_point).

ggsave("figures/BigVsSmall.svg")

Saving 7.29 x 4.5 in image

ggsave("figures/BigVsSmall.png")

Saving 7.29 x 4.5 in image

Flux small and flux tot track so closely because particle fractal dimension alpha, plus flux fractal dimension, gamma > |psd|. since the size distribution of the flux sould be PSD + ag (psd is negative in this case). Yo ucan see the variance at the one depth where psd is flatest at the very top.

Figure S4

Example particle size distributions

eg_dataline <- bds %>% 
  filter(profile == "stn_043", depth == 162.5)
eg_slope =  eg_dataline %>% pull(psd)
eg_icp = eg_dataline %>% pull(icp)
eg_vol = eg_dataline %>% pull(vol)

eg_datablock <- bes %>%
  filter(profile == "stn_043", depth == 162.5)


eg_lb = eg_datablock$lb
eg_binsize = eg_datablock$binsize
eg_nnp = exp(eg_icp + log(eg_lb) * eg_slope)

eg_np = eg_nnp * eg_binsize
eg_tp = eg_np * eg_vol
eg_df <- tibble(lb = eg_lb, n_nparticles = eg_nnp, nparticles = eg_np, TotalParticles = eg_tp)


EgNNP <- eg_datablock %>%
  ggplot(aes(x = lb, y = n_nparticles)) + geom_point() + scale_x_log10() + scale_y_log10() + 
  geom_path(data = eg_df) + labs(y = "Binsize & Volume Normalized \n Particles (#/L/mm)", x = "Size (mm)")

EgNP <- eg_datablock %>%
  ggplot(aes(x = lb, y = nparticles)) + geom_point() + scale_x_log10() + scale_y_log10() + 
  geom_path(data = eg_df) + labs(y = "Normalized Particles" , x = "Size (mm)")

EgTP <- eg_datablock %>%
  ggplot(aes(x = lb, y = TotalParticles)) + geom_point() + scale_x_log10() + scale_y_log10() + 
  geom_path(data = eg_df) + labs( y = "Total Particles Observed (#)", x = "Size (mm)")

plot_grid(EgNNP, EgTP, labels = c("A", "B"))

Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axis

ggsave("figures/ExamplePSD163m.png")

Saving 7.29 x 4.5 in image

ggsave("figures/ExamplePSD163m.svg")

Saving 7.29 x 4.5 in image

Figure 5A

Flux attenuation with respect ot depth and time. All extrapolated from the UVP and traps combined.

scientific_10 <- function(x) {parse(text=gsub("e\\+*", " %*% 10^", scales::scientific_format()(x))) }
scientific_10_b <- function(x) {parse(text=gsub("e\\+*", " %*% 10^", scales::scientific_format()(x))) }

scientific_10_c <- function(x) {
    xout <- gsub("1e", "10^{", format(x),fixed=TRUE)
    xout <- gsub("{-0", "{-", xout,fixed=TRUE)
    xout <- gsub("{+", "{", xout,fixed=TRUE)
    xout <- gsub("{0", "{", xout,fixed=TRUE)
    xout <- paste(xout,"}",sep="")
    return(parse(text=xout))
    
}

scale_x_log10nice <- function(name=NULL,omag=seq(-10,20),...) {
    breaks10 <- 10^omag
    scale_x_log10(breaks=breaks10,labels=scientific_10_c(breaks10),...)
}


#https://stackoverflow.com/questions/10762287/how-can-i-format-axis-labels-with-exponents-with-ggplot2-and-scales
#jacob_magnitude <- function(x){expression(10^round(log10(x)))}

cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltFlx <- bds %>% filter(project == "ETNP") %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = Flux_Smooth, shape = factor(day(time)), fill = hour(time), group = factor(time)))  + geom_point(size = 2, stroke = 1)+
  #geom_path() +
  scale_y_reverse(limits = c(1000, 0))+
  scale_x_log10nice()+
  #scale_x_log10() + 
   scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + 
  scale_fill_gradientn(name = "Hour of Day", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  
  labs(x = bquote(Smoothed~Flux~(µmol~C/m^2/d)), y = "Depth (m)") +
  #labs(x = "moo", y = "Depth (m)") +
  geom_rect(data = data.frame(project = "ETNP"), aes(xmin = 20, xmax = 180, ymin = 75, ymax = 500), colour = "red", fill = NA, inherit.aes = FALSE) +
  theme(axis.text.x = element_text(angle = 90, vjust = .3), legend.spacing = unit(.1, "cm")) +
   geom_segment(aes(y = 160, yend = 160, x = 20, xend = 500), color = "darkgreen", stroke = 0.5)+
   geom_segment(aes(y = OMZBase, yend = OMZBase, x = 20, xend = 500), color = "darkblue", stroke = 0.5)#+ geom_hline(yintercept = OMZBase, color = "darkblue")

Ignoring unknown parameters: strokeIgnoring unknown parameters: stroke

pltFlxNoLegend <- pltFlx + theme(legend.position = "none")
pltFlxLegend <- get_legend(pltFlx)

Removed 14 rows containing missing values (geom_point).

pltFlx

#plotly::ggplotly(plt1)

Figure 5B

Zooming in on where the action is happening

cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltFlxZoom <- bds %>% filter(project == "ETNP" & depth <= 500 & depth >= 75) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = Flux_Smooth, shape = factor(day(time)), fill = hour(time), group = factor(time))) + geom_point(size = 2, stroke = 1)+
  #geom_path() +
  scale_y_reverse()+
  #scale_x_log10() +
  scale_x_log10(breaks = c(seq(from = 20, to = 50, by = 10), seq(from = 60, to = 180, by = 20)), limits = c(20, 180)) +
   scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(values = rep(21:25, 2)) + 
  scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  theme(axis.text.x = element_text(angle = 90)) +
labs(x = "Smoothed Flux", y = "Depth") + theme(legend.position = "none")+
geom_hline(yintercept = 160, color = "darkgreen")

pltFlxZoom

#plotly::ggplotly(plt1)

Figure 5C

Rate of change of flux, taken to the fifth root so one can see patterns.

cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltDelta3 <- bds %>% filter(project == "ETNP") %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = pracma::nthroot(DF/DZ, 5), shape = factor(day(time)), fill = hour(time), group = factor(time)))  + geom_point(size = 2, stroke = 1)+
  #geom_path() +
  scale_y_reverse(limits = c(1000, 0))+
  scale_x_continuous(limits = c(-2.1, .6), breaks = seq(from = -2, to = .75, by = 0.5)) +
  #scale_x_log10() +
   scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + 
  scale_fill_gradientn(name = "Hour", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  geom_vline(xintercept = 0) +
  labs(x = bquote((DF/DZ)^{1/5}~(µmolC/m^3/d)^{1/5}), y = "Depth (m)") + theme(legend.pos = "none")+
  geom_hline(yintercept = 160, color = "darkgreen") + geom_hline(yintercept = OMZBase, color = "darkblue")
  #labs(x = "(DF/DZ) ^ 1/5 (µmol C/m^3/d) ^ 1/5")

pltDelta3

#plotly::ggplotly(plt1pos)

Combining the plots

Within panel drawing

pgTop <- ggdraw(pltFlxNoLegend 
       ) +
  draw_plot(pltFlxZoom, .4, .25, .55, .60) +
  draw_plot_label(
    c("","B"),
    c(.05, 0.55),
    c(1, 0.85),
    size = 16
  )

Removed 14 rows containing missing values (geom_point).

pgTop

pgBottom <- plot_grid(pltDelta3, pltFlxLegend , rel_widths = c(3, 1), labels = c(“C”, ""), label_size = 14)

I don’t know whats going on below here

pgBottom <- pltDelta3  + geom_rect(aes(xmin = -2, xmax = -1.15, ymin = 170, ymax = 1000), colour = "gray50", fill = "white", inherit.aes = FALSE) + draw_plot(pltFlxLegend , -1.9, -575, .7)
pgBoth <- plot_grid(pgTop + theme(plot.margin = unit(c(0, 0, 0, 0), units = "cm")),
                    pgBottom + theme(plot.margin = unit(c(0, 0, 0, 0), units = "cm")),
                    ncol = 1, rel_heights = c(4, 4), labels = c("A", "C"), label_size = 16)

Removed 33 rows containing missing values (geom_point).

pgBoth

ggsave("figures/FluxDeepDive.png")

Saving 5 x 9 in image

ggsave("figures/FluxDeepDive.svg")

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Summary stats

Test for day to day and hourly variability in rate of change of flux (fifth root transformed)

bdsAddTime <- bds %>% 
  mutate(Hour = hour(time), Day = day(time))

DFG1 <- gam(pracma::nthroot(DF/DZ, 5)~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 250 & depth <=500 & project == "ETNP"))

DFG2 <- gam(pracma::nthroot(DF/DZ, 5) ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 250 & depth <=500 & project == "ETNP"))

DFG3 <- gam(pracma::nthroot(DF/DZ, 5) ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 250 & depth <=500 & project == "ETNP"))

DFG_DayOnly <- gam(pracma::nthroot(DF/DZ, 5) ~  s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 250 & depth <=500 & project == "ETNP"))

DFG_NoFifth <- gam(pracma::nthroot(DF/DZ, 1)~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 250 & depth <=500 & project == "ETNP"))

summary(DFG1)

Family: gaussian 
Link function: identity 

Formula:
pracma::nthroot(DF/DZ, 5) ~ s(depth, k = 3) + s(Day, k = 3) + 
    s(Hour, k = 4, bs = "cc")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept) -0.18385    0.05927  -3.102   0.0037 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
            edf Ref.df     F p-value  
s(depth) 1.6736  1.893 3.885  0.0635 .
s(Day)   1.9071  1.991 5.159  0.0132 *
s(Hour)  0.9147  2.000 0.922  0.1473  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.272   Deviance explained = 35.2%
GCV = 0.16976  Scale est. = 0.14755   n = 42

summary(DFG_DayOnly)

Family: gaussian 
Link function: identity 

Formula:
pracma::nthroot(DF/DZ, 5) ~ s(Day, k = 3)

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept) -0.18385    0.06436  -2.857  0.00682 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
         edf Ref.df     F p-value  
s(Day) 1.879  1.985 4.024  0.0299 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.142   Deviance explained = 18.1%
GCV = 0.18677  Scale est. = 0.17396   n = 42

summary(DFG_NoFifth)

Family: gaussian 
Link function: identity 

Formula:
pracma::nthroot(DF/DZ, 1) ~ s(depth, k = 3) + s(Day, k = 3) + 
    s(Hour, k = 4, bs = "cc")

Parametric coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.028354   0.007893  -3.592 0.000941 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
               edf Ref.df     F p-value   
s(depth) 1.809e+00  1.963 9.249 0.00138 **
s(Day)   1.855e+00  1.979 3.028 0.05229 . 
s(Hour)  1.625e-08  2.000 0.000 0.77687   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.346   Deviance explained = 40.5%
GCV = 0.0029435  Scale est. = 0.0026167  n = 42

# summary(DFG2)
# summary(DFG3)
# 
# summary(DFG1)$r.sq - summary(DFG2)$r.sq
# summary(DFG2)$r.sq - summary(DFG3)$r.sq
# summary(DFG3)$r.sq

There is variability with respect to depth, and day and hour Depth p = 0.03 R^2 = 0.088. Add affect of day p = 0.004, extra R^2 = 0.11, Add affect of hour p = 0.02 extra R2 = 0.12

Plot of the gams above

#plot.new()
FluxGamPlot <- function(){
  par(mfrow = c(2,2))
  plot(DFG1)
  mtext(expression(bold("C")), side = 3, line = 0, adj = 0, cex = 2)
  par(mfg = c(1,1))
  mtext(expression(bold("A")), side = 3, line = 0, adj = 0, cex = 2)
  par(mfg = c(1,2))
  mtext(expression(bold("B")), side = 3, line = 0, adj = 0, cex = 2)
}

FluxGamPlot()

png(filename = "./figures/FluxGamPlot.png", width = 10, height = 8, units = "in", res = 200)

FluxGamPlot()
dev.off()

png 
  2 

Figure 7

Difference from model expectations

(u mol C / m^3 / day)

disagFig <- bds %>% filter(project == "ETNP") %>%
  ggplot(aes(y = depth, x = pracma::nthroot(ospsDZ, 3), shape = factor(day(time)), fill = hour(time), group = factor(time))) + geom_point(size = 2) + scale_y_reverse(limits = c(1000, 0)) +
  scale_x_continuous(limits = c(-1, 1)) +
  geom_vline(xintercept = 0) +   scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) +
  #labs(x = bquote("Observed - Modeled Small Particle Flux"~(μmol/m^3/day)), y = "Depth (m)") +
  labs(x = paste("Deviation from Model", expression((μmol/m^3/day)))) +
  scale_fill_gradientn(name = "Hour of Day", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) + geom_hline(yintercept = PhoticBase, color = "darkgreen") + geom_hline(yintercept = OMZBase, color = "darkblue")
disagFig

#ggsave("..figures/FluxSizeShift.svg"

 ggsave("figures/FluxSizeShift.png")

Saving 6 x 4 in image

 ggsave("figures/FluxSizeShift.svg")

Saving 6 x 4 in image

Summary statistics

bdsAddTime <- bds %>%
  mutate(Hour = hour(time), Day = day(time))

OZG1 <- gam(ospsDZ ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

OZG2 <- gam(ospsDZ ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

OZG3 <- gam(ospsDZ ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= PhoticBase & depth <=500 & project == "ETNP"))

summary(OZG1)

Family: gaussian 
Link function: identity 

Formula:
ospsDZ ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.08042    0.01038   7.745 9.85e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
           edf Ref.df      F  p-value    
s(depth) 1.593  1.835 19.484 3.84e-06 ***
s(Day)   1.872  1.983  7.744 0.000651 ***
s(Hour)  1.420  2.000  2.473 0.036582 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.428   Deviance explained = 46.9%
GCV = 0.0081324  Scale est. = 0.0074388  n = 69

summary(OZG2)

Family: gaussian 
Link function: identity 

Formula:
ospsDZ ~ s(depth, k = 3) + s(Day, k = 3)

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.08042    0.01077    7.47 2.65e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
           edf Ref.df     F  p-value    
s(depth) 1.562  1.808 18.46 7.69e-06 ***
s(Day)   1.897  1.989  5.96  0.00314 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.385   Deviance explained = 41.6%
GCV = 0.0085495  Scale est. = 0.0079971  n = 69

summary(OZG3)

Family: gaussian 
Link function: identity 

Formula:
ospsDZ ~ s(depth, k = 3)

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.08042    0.01169   6.877 2.59e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
           edf Ref.df     F  p-value    
s(depth) 1.453    1.7 17.31 2.23e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.274   Deviance explained =   29%
GCV = 0.0097831  Scale est. = 0.0094354  n = 69

summary(OZG1)$r.sq - summary(OZG2)$r.sq # Extra from Hour

[1] 0.04294176

summary(OZG2)$r.sq - summary(OZG3)$r.sq # Extrafrom Day

[1] 0.1106415

summary(OZG3)$r.sq # Depth

[1] 0.2741944

Plot of those gams Figure S10

OSMSGamPlot <- function(){
  par(mfrow = c(2,2))
  plot(OZG1)
  
  par(mfg = c(1,1))
  mtext(expression(bold("A")), side = 3, line = 0, adj = 0, cex = 2)
  par(mfg = c(1,2))
  mtext(expression(bold("B")), side = 3, line = 0, adj = 0, cex = 2)
  par(mfg = c(2,1))
  mtext(expression(bold("C")), side = 3, line = 0, adj = 0, cex = 2)
}

OSMSGamPlot()

png(filename = "./figures/OSMSGamPlot.png", width = 10, height = 8, units = "in", res = 200)

OSMSGamPlot()

dev.off()

png 
  2 

Figure 3

Trap data

trapFlux3 <- read_csv("dataOut/fluxMS_distilled.csv")

Parsed with column specification:
cols(
  Class = col_character(),
  Depth = col_double(),
  TrapID = col_character(),
  TrapType = col_character(),
  SampleType = col_character(),
  C_flux = col_double(),
  C_flux_umol = col_double()
)

UVPFluxComb <- read_csv("dataOut/CombinedProfileFluxEst_DS.csv")

Parsed with column specification:
cols(
  depth = col_double(),
  Flux = col_double()
)

UVPFluxOE <- read_csv("dataOut/ObservedVsExpectedFlux.csv")

Parsed with column specification:
cols(
  depth = col_double(),
  tn_flux = col_double(),
  profile = col_character(),
  project = col_character(),
  time = col_character(),
  tot_flux2 = col_double()
)

fluxMS_distilled_toPlot <- trapFlux3 %>%
  mutate(SampleType = recode(SampleType, `plus.p` = "plus-particles", top = "top-collector"))

Remove traps where mass spec didn’t work correctly 2-17 150 1-12 73m 1-12 148 2-14 100 |(TrapID == “2-17” & Depth == 150)

fluxMS_distilled_toPlot2 <- fluxMS_distilled_toPlot %>%
 filter(!((TrapID == "1-12") | (TrapID == "2-14" & Depth == 100)|(TrapID == "2-17" & Depth == 150)))
#fluxMS_distilled_toPlot2

Traps where mass spec didn’t work.

UVPFluxPlot00 <- UVPFluxComb %>% 
  ggplot(aes(y = depth))  + scale_y_reverse(limits = c(1000, 0)) +
  scale_x_continuous(limits = c(0, 200)) +
  geom_point(aes(y = Depth, x = C_flux_umol, shape = TrapType, ID = TrapID),
             colour = "black", stroke = 1, size = 5, data = fluxMS_distilled_toPlot2) +
  geom_line(aes(x = Flux), size = 1, color = "black") +
  geom_point(aes(x = -1, y = -1, size = "UVP Estimate")) + # dummy point for the legend
  geom_point(aes(x = tot_flux2), size = 3, shape = 21, color = "white", fill = "black", data = UVPFluxOE) +
scale_shape_manual(values = c(25, 22))+
  scale_size_manual(values = 1, name = "") +
  ylab("Depth (m)") +
  #xlab(expression(Flux µmolC/m^2/day)) +
  xlab(expression(paste("x axis ", ring(A)^2))) +
  xlab(expression(paste("Flux (µ mol C/", m^2, "/day)"))) +
  
  guides(fill = guide_legend(override.aes = list(shape = 21))) +
  theme_cowplot() + 
  theme(
        legend.position = c(0.5, 0.4),
        legend.box.background = element_rect(color = "black", size = 0.5),
        legend.margin = margin(-10, 5, 10, 5)
  ) 

Ignoring unknown aesthetics: ID

# UVPFluxPlot <- UVPFluxPlot00 +
#   geom_rect(data = data.frame(project = "ETNP"), aes(xmin = 15, xmax = 32, ymin = 45, ymax = 195), colour = "red", fill = NA, inherit.aes = FALSE)

UVPFluxPlot00

ggsave("figures/FittedFlux.png")

Saving 7.29 x 4.5 in image

ggsave("figures/FittedFlux.svg")

Saving 7.29 x 4.5 in image

Figure S2

Example particle size distribution

TPPlot <- bes %>% filter(profile == "stn_043") %>% group_by(lb) %>% ggplot(aes(x = TotalParticles, y = depth, col = log(lb), group = lb)) + scale_y_reverse(limits = c(1000, 0)) + geom_point() + scale_x_log10() + scale_color_viridis_c() + geom_path() + geom_vline(xintercept = 1) + geom_vline(xintercept = 5) + labs(y = "Depth (m)", x = "TotalParticles Observed (#)")

nnpPlot <- bes %>% filter(profile == "stn_043") %>% group_by(lb) %>% ggplot(aes(x = n_nparticles, y = depth, col = log(lb), group = lb)) + scale_y_reverse(limits = c(1000, 0)) + geom_point() + scale_x_log10() + scale_color_viridis_c() + geom_path() + geom_vline(xintercept = 1) + geom_vline(xintercept = 5) + labs(y = "Depth (m)", x = "Binsize and Volume Normalized Particles (#/L/mm)")

FitPlot <- bes %>% filter(profile == "stn_043") %>% group_by(lb) %>% ggplot(aes(x = nnp_smooth, xmin = nnp_lower, xmax = nnp_upper, y = depth, col = log(lb), group = lb)) + scale_y_reverse(limits = c(1000, 0)) + geom_point() + scale_x_log10() + scale_color_viridis_c() + geom_path() + geom_vline(xintercept = 1) + geom_vline(xintercept = 5) + labs(y = "Depth (m)", x = "Smoothed - Normalized Particles (#/L/mm)") + geom_errorbar(width = 10, alpha = 0.5)

npLegend <- get_legend(FitPlot + theme(legend.box.margin = margin(0, 0, 40, 200)) + labs(col = expression(log[e](Size (mm)))))

Removed 325 rows containing missing values (geom_point).Removed 325 row(s) containing missing values (geom_path).

plot_grid(
  TPPlot + theme(legend.position = "none"),
  nnpPlot + theme(legend.position = "none"),
  npLegend ,
  FitPlot + theme(legend.position = "none")
)

Transformation introduced infinite values in continuous x-axisTransformation introduced infinite values in continuous x-axisRemoved 325 rows containing missing values (geom_point).Removed 325 row(s) containing missing values (geom_path).Transformation introduced infinite values in continuous x-axisTransformation introduced infinite values in continuous x-axisRemoved 325 rows containing missing values (geom_point).Removed 325 row(s) containing missing values (geom_path).Removed 325 rows containing missing values (geom_point).Removed 325 row(s) containing missing values (geom_path).

ggsave("figures/AllParticleSizes.svg")

Saving 10 x 6.18 in image

ggsave("figures/AllParticleSizes.png")

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Figure 6

Weber Bianchi Figs

A bunch of upfront smoothing

SameGam <- gam(TotalParticles ~s(log(lb), log(depth), by = factor(time)), offset = log(vol * binsize), family = nb(),
    data = bes %>% filter(project == "ETNP"))

besE <- bes %>% filter(project == "ETNP")

lb_new <- exp(seq(from = log(0.1), to = log(2.1), by = 0.05))
ub_new <- lead(lb_new)
binsize_new <- ub_new - lb_new

lbbs <- tibble(lb = lb_new, ub = ub_new, binsize = binsize_new)

Expanded <- expand_grid(lb = exp(seq(from = log(0.1), to = log(2), by = 0.05)), depth = seq(from = 20, to = 2000, by = 20), time = as.factor(unique(besE$time))) %>% left_join(lbbs, by = "lb")

Pred <- exp(predict(SameGam, Expanded))
ToPlot <- bind_cols(Expanded, nnparticles = Pred) %>% mutate(time = as.character(time)) %>% mutate(nparticles = nnparticles * binsize)

meanBese <- ToPlot %>% filter(lb <= 2) %>% group_by(lb, depth) %>% summarize(nparticles = mean(nparticles), nnparticles = mean(nnparticles))

WBColorMap <- meanBese%>%
   ggplot(aes(x = lb, y = depth, fill = log10(nnparticles), z = log10(nnparticles))) + geom_tile() + scale_fill_viridis_c(name = "log10(number density \n (normalized))") + scale_y_reverse() + scale_x_log10() + geom_contour(color = "black") + geom_hline(yintercept = 160, color = "darkgreen") + geom_hline(yintercept = OMZBase, color = "darkblue")
WBColorMap

mbGam <- meanBese %>% group_by(depth)  %>% nest() %>%
  mutate(mod = map(data, ~gam(log(nnparticles) ~ log(lb), family = gaussian(), data = .))) %>% 
  mutate(psd = map_dbl(mod, ~summary(.)$p.coeff[2]))

Particle size distribution, smoothed over all stations

pWBPSD <- mbGam %>% ggplot(aes(x = psd, y = depth)) + geom_path() + scale_y_reverse()  + geom_hline(yintercept = 160, color = "darkgreen") + geom_hline(yintercept =  OMZBase, color = "darkblue")
pWBPSD

Small particles biomass

PubDf <- ToPlot %>% mutate(ubiomass = nparticles * lb ^ ag_global) %>% filter(lb < 0.5) %>% group_by(time, depth) %>% summarize(ubiomass = sum(ubiomass)) %>% ungroup %>% group_by(depth)  %>% summarise(ubiomass = mean(ubiomass))
photicBiomass <- PubDf %>% filter(depth <= 165, depth >= 155) %>% summarize(ubiomass = mean(ubiomass)) %>% pull(ubiomass)
PubDf <- PubDf %>% mutate(nbiomass = ubiomass/photicBiomass)
pWBS <- PubDf %>% ggplot(aes(x = nbiomass, y = depth)) + geom_path() + scale_y_reverse() + scale_x_continuous(limits = c(0,1.2)) + geom_hline(yintercept = 160, color = "darkgreen") + geom_vline(xintercept = 1, color = "gray50") + geom_vline(xintercept = 0, color = "gray50") + geom_hline(yintercept = OMZBase, color = "darkblue") + labs( x = "Small particle mass (norm.)")
pWBS

Large Particles Biomass

LubDf <- ToPlot %>% mutate(ubiomass = nparticles * lb ^ ag_global) %>% filter(lb >= 0.5) %>% group_by(time, depth) %>% summarize(ubiomass = sum(ubiomass)) %>% ungroup %>% group_by(depth)  %>% summarise(ubiomass = mean(ubiomass))
photicBiomass <- LubDf %>% filter(depth <= 165, depth >=155) %>% summarize(ubiomass = mean(ubiomass)) %>% pull(ubiomass)
LubDf <- LubDf %>% mutate(nbiomass = ubiomass/photicBiomass)
pWBL <- LubDf %>% ggplot(aes(x = nbiomass, y = depth)) + geom_path() + scale_y_reverse() + scale_x_continuous(limits = c(0,1)) + geom_hline(yintercept = 160, color = "darkgreen") + labs( x = "Large particle mass (norm.)") + geom_vline(xintercept = 1, color = "gray50") + geom_vline(xintercept = 0, color = "gray50") + geom_hline(yintercept = OMZBase, color = "darkblue")
pWBL

Combine the three lower pannels

WBFig5 <- plot_grid(pWBPSD, pWBS,pWBL, nrow = 1, labels = c("B", "C", "D"))

Removed 6 row(s) containing missing values (geom_path).Removed 7 row(s) containing missing values (geom_path).

WBFig5

Four panel figure of Weber and Bianchi equivalent data

WBcombined <- plot_grid(WBColorMap + theme(plot.margin = unit(c(0,3,0, 3), "cm")), WBFig5, ncol = 1, labels = c("A", ""))
WBcombined

ggsave("figures/WBModelValidation.png")

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Figure S8

P16 Flux

Flux

scientific_10 <- function(x) {parse(text=gsub("e\\+*", " %*% 10^", scales::scientific_format()(x))) }
#https://stackoverflow.com/questions/10762287/how-can-i-format-axis-labels-with-exponents-with-ggplot2-and-scales
#jacob_magnitude <- function(x){expression(10^round(log10(x)))}

cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltFlxP16 <- bds %>% filter(project == "P16") %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = Flux_Smooth, group = factor(time)))  + geom_point(size = 3, stroke = 1)+
  geom_path() +
  scale_y_reverse(limits = c(1000, 0))+
  scale_x_log10(limits = c(35, 150),breaks = seq(from = 20, to = 150, by = 20)) +
   scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + 
  scale_fill_gradientn(name = "Hour of Day", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  
labs(x = bquote(Smoothed~Flux~(µmol~C/m^2/d)), y = "Depth (m)") +
  geom_hline(yintercept = 200, color = "darkgreen") +
  theme(axis.text.x = element_text(angle = 90, vjust = .3), legend.spacing = unit(.1, "cm"))
# 
# 
# 
# pltFlxNoLegend <- pltFlx + theme(legend.position = "none")
# pltFlxLegend <- get_legend(pltFlx)
# 
pltFlxP16

# #plotly::ggplotly(plt1)

Rate of change of flux – fifth root transformed

cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltDelta3P16 <- bds %>% filter(project == "P16") %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = pracma::nthroot(DF/DZ, 5), group = factor(time)))  + geom_point(size = 3, stroke = 1)+
  geom_path() +
  scale_y_reverse(limits = c(1000, 0))+
  scale_x_continuous(limits = c(-1, .1), breaks = seq(from = -2, to = .75, by = 0.5)) +
  #scale_x_log10() +
   scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + 
  scale_fill_gradientn(name = "Hour", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 200, color = "darkgreen")+
  labs(x = bquote((DF/DZ)^{1/5}~(µmolC/m^3/d)^{1/5}), y = "Depth (m)") + theme(legend.pos = "none")
  #labs(x = "(DF/DZ) ^ 1/5 (µmol C/m^3/d) ^ 1/5")

pltDelta3P16

#plotly::ggplotly(plt1pos)

Difference from model

osms_p16 <- bds %>% filter(project == "P16") %>%
  ggplot(aes(y = depth, x = pracma::nthroot(ospsDZ, 3), group = factor(time))) + geom_point(size = 3) + geom_path() + scale_y_reverse(limits = c(1000, 0)) +
  scale_x_continuous(limits = c(-1, 1)) +
  geom_vline(xintercept = 0) +   scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + labs(x = "Observed - Modeled Small Particle Flux \n µmol/m^3/day") +
  scale_fill_gradientn(name = "Hour of Day", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) + geom_hline(yintercept = PhoticBase, color = "darkgreen") 
plotly::ggplotly(osms_p16)

#ggsave("..figures/FluxSizeShift.svg"

Combine everything together

plot_grid(
  pltFlxP16,
  pltDelta3P16,
  osms_p16
)

Removed 28 rows containing missing values (geom_point).Removed 28 row(s) containing missing values (geom_path).Removed 32 rows containing missing values (geom_point).Removed 32 row(s) containing missing values (geom_path).Removed 29 rows containing missing values (geom_point).Removed 29 row(s) containing missing values (geom_path).

ggsave("figures/P16FluxRelate.svg")

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ggsave("figures/P16FluxRelate.png")

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Figure S10

Flux attenuation example

Take one profile, attenuate it, and show what it looks like

source("ModelStuff.R")

Parsed with column specification:
cols(
  Cruise = col_character(),
  Station = col_character(),
  `mon/dd/yyyy` = col_character(),
  `hh:mm` = col_time(format = ""),
  `Longitude [degrees east]` = col_double(),
  `Latitude [degrees north]` = col_double(),
  `Bottom Depth [m]` = col_double(),
  `Pressure [db]` = col_double(),
  `Temperature [degrees C]` = col_double(),
  `Temperature 2 [degrees C]` = col_double(),
  `Salinity [psu]` = col_double(),
  `Salinity 2 [psu]` = col_double(),
  `Fluorescense [mg/m^3]` = col_double(),
  `Beam Transmission [%]` = col_double(),
  PAR = col_double(),
  `Oxygen [umol/kg]` = col_double(),
  `Oxygen [% saturation]` = col_double()
)

scan_for_example <- bds %>% filter(project == "ETNP", depth < 500, depth > 200) %>% select(profile, depth, DFP, use_this_DFP, ospsDZ)

#loc_station = "stn_036"
loc_station = "stn_043"
loc_depth = 225
loc_prev_depth = 112.5

allDFPs <- bds %>% filter(profile == loc_station, depth >= loc_prev_depth, depth <= loc_depth) %>% summarize(DFP = prod(DFP), use_this_DFP = prod(use_this_DFP))

loc_DFP <-  allDFPs %>% pull(DFP)
loc_use_DFP <- allDFPs %>% pull(use_this_DFP)


for_single_disag <- bes %>% filter(profile == loc_station, depth %in% c(loc_prev_depth, loc_depth)) %>% select(depth, lb, nnp_smooth) %>%
  mutate(depth = recode(depth, `112.5` = "Shallow", `225` = "Deep")) %>% # I have no idea how to not hard code this bit
  pivot_wider(names_from = depth, values_from = nnp_smooth) 

with_disag <- for_single_disag %>%
  mutate(Predicted_Deep = remin_smooth_shuffle(Shallow, loc_use_DFP)) 
#remin_smooth_shuffle(for_single_disag$Shallow,loc_use_DFP)

for_plot_disag <- with_disag %>% pivot_longer(cols = -lb) %>% #filter(lb <= 5) %>%
  mutate(name = factor(name, levels = c("Shallow", "Deep", "Predicted_Deep"))) %>%
  mutate(name = recode_factor(name, Shallow = "Shallow (112.5m)", Deep = "Deep (225m)", Predicted_Deep = "Predicted Deep (225m)"))

for_plot_disag %>% ggplot(aes(x = lb, y = value, shape = name)) + geom_point() + scale_x_log10() + scale_y_log10() + scale_shape_manual(values = c(1, 6, 3)) + theme(legend.title = element_blank()) + labs(x = "Particle Size (mm)", y = "Normalized Particle Abundance (#/L/mm)")

ggsave("figures/DisagExample.png")

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ggsave("figures/DisagExample.svg")

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